The world-wide spreading of coronavirus disease (COVID-19) has greatly shaken human society, thus effective and fast-speed methods of non-daily-life-disturbance sterilization have become extremely significant. In this work, by fully benefitting from high-quality AlN template (with threading dislocation density as low as ?6×108 cm-2) as well as outstanding deep ultraviolet (UVC-less than 280 nm) light-emitting diodes (LEDs) structure design and epitaxy optimization, high power UVC LEDs and ultra-high-power sterilization irradiation source are achieved. Moreover, for the first time, a result in which a fast and complete elimination of SARS-CoV-2 (the virus causes COVID-19) within only 1 s is achieved by the nearly whole industry-chain-covered product. These results advance the promising potential in UVC-LED disinfection particularly in the shadow of COVID-19.This article describes the methodology and the possibilities of collecting operation data in a mobile network provider. First, the architecture and the principles used in the system are described. The precision analysis of the population commuting in the region and during the pandemic and nonpandemic times. Moreover, several ideas about further utilization of the data will be formulated and described. Finally, a graph-based approach that describes the creation of the community structure between the people and the means of its analysis.Certain Health Workers (HWs) may have inadequate knowledge and perceptions regarding COVID-19. As a result, they may not be completely aware of the danger/risk involved, which could impact their ability to control the spread of the virus. This systematic review aims to enhance HWs' knowledge and their perception of the spread risk of COVID-19 during the pandemic. A search was conducted in four databases (Medline, CINAHL, Scopus, and ScienceDirect) to locate peer-reviewed studies published in English between January 2020 and April 2020. Eventually, nine articles satisfied the inclusion criteria and were, therefore, included in the present study. Six of the aforementioned studies specifically investigated HWs' perception of risk. Apart from a study that indicated medium perception (min = 56.5%), all other studies found high levels of risk perception (n = 5, max 92.1%). As for HWs' knowledge, apart from two studies that indicated medium percentage levels (min = 56.5%), the rest of the studies report high percentages (n = 7, max = 93.2%). Two of the studies, which assessed the sources of information that HWs use, agree that social media is the most widely used source of information. The findings of this study suggest that HWs had a satisfactory perception of risk during the spread of COVID-19. Although fields with medium knowledge levels were identified, HWs' overall knowledge may also be described as satisfactory. It is also noted that certain demographic characteristics (occupation, age, and years of experience) appear to affect HWs' knowledge and perceptions. The application of educational strategies aiming to provide continuous support to HWs is unanimously recommended by all studies.This article mainly explores the economic and health challenges faced by Bangladesh amid COVID-19 and the policies taken by the government of Bangladesh to tackle the economic and health issues. Bangladesh is ranked as one of the worst-hit countries in terms of total corona infections. Affecting the social, economic, and health sectors of the country, COVID-19 pandemic has dampened the overall economic well-being and thus GDP growth along with skyrocketing poverty, inequality, and unemployment nationwide. To tackle these crises, the government has initiated effective policy measures which, in turn, enhanced the recovery rate of COVID-19 positive patients and strengthened the recovery of economic indicators. Therefore, this article suggests other hard-hit COVID-19 affected countries following the recovery model of Bangladesh to encounter the economic and health challenges due to the coronavirus pandemic.The study aims at demonstrating how social communication has changed in terms of flows and content in the different phases of the COVID-19 pandemic to get to the fact that public administrations have embarked on a path of rapprochement with the citizen that starts from the methods of communication and interaction. This article presents an exploratory and multidisciplinary study conducted through the analysis of the Facebook page of the Italian municipalities with the highest Covid19-induced mortality rates (Piacenza, Bergamo, Lodi, Cremona, Brescia, Pavia, Parma, Mantova, Alessandria, Lecco and Sondrio). Fanpage Karma has been used to conduct the investigation and get the analytics. Local governments are implementing a process of gradual approach to the needs of the citizen and learning new ways of communication. In the conclusion of our study - conducted at the time of the pandemic - we can affirm that local governments are in an early stage of the process both for the acquisition of skills for social communication and for the definition of a communication strategy to strengthen their social identity aware of the fact that the agile and lean communication makes the citizen much more informed and involved in city life than traditional communication. This paper analyses a social network like Facebook as a not common tool for local government's communication in a period of severe emergency. A multidisciplinary approach is adopted as a distinctive factor. The focus is on the contribution of social communication on citizens' engagement.The outbreak of Coronavirus 2019 (COVID-19) has impacted everyday lives globally. The number of positive cases is growing and India is now one of the most affected countries. This paper builds predictive models that can predict the number of positive cases with higher accuracy. https://www.selleckchem.com/products/bay-2416964.html Regression-based, Decision tree-based, and Random forest-based models have been built on the data from China and are validated on India's sample. The model is found to be effective and will be able to predict the positive number of cases in the future with minimal error. The developed machine learning model can work in real-time and can effectively predict the number of positive cases. Key measures and suggestions have been put forward considering the effect of lockdown.